Questions tagged [lightgbm]

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t-statistics in gradient boosted machine/forest such LightGBM

Is there a t-statistics in the gradient boosted forest regression model such as that in LightGBM? If so, how is it defined, extracted and used?
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How can I measure the importance of a leaf in LightGBM?

I want to understand, which leaves in my lightgbm model have low importance (If I deleted the leaf, the model wouldn't become "worse"). Which approaches exists for it? Thank you in advance!
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how does `subsample` parameter work in boosting algorithms like xgboost and lightgbm?

From what I know, both of them are sequential learners and only the 1st tree in the sequence gets built on the data and all the following trees that get built are to correct the mistakes of previous ...
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Lightgbm, time-series and spikes repeated on a yearly basis

I have a data set (time-series) with the shape {$2190$x$63$}. There are 63 variables, 2 products ($A$ and $B$) worth of 3 years of daily data, thus I have $1095$ observations per product and total of $...
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When we use k-means clustering with Light GBM, comparing with Random Forest

I am developping the prediction model with many parameters. As I was not satisfied by the performance of Random Forest Regression, I tried to use k-means clustering to regroup the similar variable and ...
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How to fix the tree structure for a tree-based algorithm?

Background Some of our BI analysts and most of our managers are interested in making explainable predictions. One of our colleagues proposed an approach based on individual tree leaves from a tree-...
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How to get the best num_boost_round on the full training data?

I have a huge training data of size 5.5 GBs with over 55m rows. Because iterating over the whole dataset again and again was too slow, I used a 1% sample of this whole data to select the best ...
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What's the difference between combined pos_bagging_fraction and neg_bagging_fraction vs is_unbalance vs scale_pos_weight in LightGBM?

Let's suppose a binary classification task and an unbalanced dataset (10% of positive records). I am using LightGBM and would like to better understand the difference between the combined ...
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Any reasons to prefer neural networks over boosting methods in tabular data?

Based on Kaggle winners data, it seems that ensemble boosting methods like XGBOOST, LIGHTGBM, CATBOOST are the top choices when dealing with structured or tabular data for maximizing the prediction ...
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Predictor With Lower Mean Absolute Error Ends Up Worse

I have been recently working on a problem to estimate the ETAs of vehicles using ensemble techniques such as LightGBM. As expected, the distance taken by the vehicle's route to its destination is a ...
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How to tune LightGBM parameters to overcome underfitting? [closed]

I'm using LightGBM for a regression task. My training data's shape is (2000000, 1600), which means the number of training data is 2 million +, and each sample has 1600 features. The figure below is ...
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Why can EFB(Exclusive Feature Bundling) works in lightGBM?

As I know, EFB can help you to decrease features which are sparse. They put two features together and add offset every feature in feature bundles. They combine features into same histogram. After ...
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Why is the Hessian of RegressionL1Loss set to 1 in LightGBM

I'm reading this code snippet related to RegressionL1loss implementation in LightGBM ...
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